@article{LaskovGehlKruegeretal.2006,
  author    = {Laskov, Pavel and Gehl, Christian and Kr{\"u}ger, Stefan and M{\"u}ller, Klaus-Robert},
  title     = {Incremental support vector learning: analysis, implementation and applications},
  series = {Journal of machine learning research},
  volume    = {7},
  journal   = {Journal of machine learning research},
  publisher = {MIT Press},
  address   = {Cambridge, Mass.},
  issn      = {1532-4435},
  pages     = {1909 -- 1936},
  year      = {2006},
  abstract  = {Incremental Support Vector Machines (SVM) are instrumental in practical applications of online learning. This work focuses on the design and analysis of efficient incremental SVM learning, with the aim of providing a fast, numerically stable and robust implementation. A detailed analysis of convergence and of algorithmic complexity of incremental SVM learning is carried out. Based on this analysis, a new design of storage and numerical operations is proposed, which speeds up the training of an incremental SVM by a factor of 5 to 20. The performance of the new algorithm is demonstrated in two scenarios: learning with limited resources and active learning. Various applications of the algorithm, such as in drug discovery, online monitoring of industrial devices and and surveillance of network traffic, can be foreseen.},
  language  = {en}
}
@article{ShenoyKrauledatBlankertzetal.2006,
  author    = {Shenoy, Pradeep and Krauledat, Matthias and Blankertz, Benjamin and Rao, Rajesh P. N. and M{\"u}ller, Klaus-Robert},
  title     = {Towards adaptive classification for BCI},
  doi       = {10.1088/1741-2560/3/1/R02},
  year      = {2006},
  abstract  = {Non-stationarities are ubiquitous in EEG signals. They are especially apparent in the use of EEG-based brain- computer interfaces (BCIs): (a) in the differences between the initial calibration measurement and the online operation of a BCI, or (b) caused by changes in the subject's brain processes during an experiment (e.g. due to fatigue, change of task involvement, etc). In this paper, we quantify for the first time such systematic evidence of statistical differences in data recorded during offline and online sessions. Furthermore, we propose novel techniques of investigating and visualizing data distributions, which are particularly useful for the analysis of (non-) stationarities. Our study shows that the brain signals used for control can change substantially from the offline calibration sessions to online control, and also within a single session. In addition to this general characterization of the signals, we propose several adaptive classification schemes and study their performance on data recorded during online experiments. An encouraging result of our study is that surprisingly simple adaptive methods in combination with an offline feature selection scheme can significantly increase BCI performance},
  language  = {en}
}
@article{BlankertzDornhegeKrauledatetal.2006,
  author    = {Blankertz, Benjamin and Dornhege, Guido and Krauledat, Matthias and M{\"u}ller, Klaus-Robert and Kunzmann, Volker and Losch, Florian and Curio, Gabriel},
  title     = {The Berlin brain-computer interface : EEG-based communication without subject training},
  issn      = {1534-4320},
  doi       = {10.1109/Tnsre.2006.875557},
  year      = {2006},
  abstract  = {The Berlin Brain-Computer Interface (BBCI) project develops a noninvasive BCI system whose key features are 1) the use of well-established motor competences as control paradigms, 2) high-dimensional features from 128-channel electroencephalogram (EEG), and 3) advanced machine learning techniques. As reported earlier, our experiments demonstrate that very high information transfer rates can be achieved using the readiness potential (RP) when predicting the laterality of upcoming left-versus right-hand movements in healthy subjects. A more recent study showed that the RP similarily accompanies phantom movements in arm amputees, but the signal strength decreases with longer loss of the limb. In a complementary approach, oscillatory features are used to discriminate imagined movements (left hand versus right hand versus foot). In a recent feedback study with six healthy subjects with no or very little experience with BCI control, three subjects achieved an information transfer rate above 35 bits per minute (bpm), and further two subjects above 24 and 15 bpm, while one subject could not achieve any BCI control. These results are encouraging for an EEG-based BCI system in untrained subjects that is independent of peripheral nervous system activity and does not rely on evoked potentials even when compared to results with very well-trained subjects operating other BCI systems},
  language  = {en}
}
@article{MeineckeZieheKurthsetal.2005,
  author    = {Meinecke, Frank C. and Ziehe, Andreas and Kurths, J{\"u}rgen and M{\"u}ller, Klaus-Robert},
  title     = {Measuring phase synchronization of superimposed signals},
  issn      = {0031-9007},
  year      = {2005},
  abstract  = {Phase synchronization is an important phenomenon that occurs in a wide variety of complex oscillatory processes. Measuring phase synchronization can therefore help to gain fundamental insight into nature. In this Letter we point out that synchronization analysis techniques can detect spurious synchronization, if they are fed with a superposition of signals such as in electroencephalography or magnetoencephalography data. We show how techniques from blind source separation can help to nevertheless measure the true synchronization and avoid such pitfalls},
  language  = {en}
}
@article{LemmCurioHlushchuketal.2006,
  author    = {Lemm, Steven and Curio, Gabriel and Hlushchuk, Yevhen and M{\"u}ller, Klaus-Robert},
  title     = {Enhancing the signal-to-noise ratio of ICA-based extracted ERPs},
  issn      = {0018-9294},
  doi       = {10.1109/Tbme.2006.870258},
  year      = {2006},
  abstract  = {When decomposing single trial electroencephalography it is a challenge to incorporate prior physiological knowledge. Here, we develop a method that uses prior information about the phase-locking property of event-related potentials in a regularization framework to bias a blind source separation algorithm toward an improved separation of single-trial phase-locked responses in terms of an increased signal-to-noise ratio. In particular, we suggest a transformation of the data, using weighted average of the single trial and trial-averaged response, that redirects the focus of source separation methods onto the subspace of event-related potentials. The practical benefit with respect to an improved separation of such components from ongoing background activity and extraneous noise is first illustrated on artificial data and finally verified in a real-world application of extracting single-trial somatosensory evoked potentials from multichannel EEG-recordings},
  language  = {en}
}
@article{LaubRothBuhmannetal.2006,
  author    = {Laub, Julian and Roth, Volker and Buhmann, Joachim and M{\"u}ller, Klaus-Robert},
  title     = {On the information and representation of non-Euclidean pairwise data},
  issn      = {0031-3203},
  doi       = {10.1016/j.patcog.2006.04.016},
  year      = {2006},
  abstract  = {Two common data representations are mostly used in intelligent data analysis, namely the vectorial and the pairwise representation. Pairwise data which satisfy the restrictive conditions of Euclidean spaces can be faithfully translated into a Euclidean vectorial representation by embedding. Non-metric pairwise data with violations of symmetry, reflexivity or triangle inequality pose a substantial conceptual problem for pattern recognition since the amount of predictive structural information beyond what can be measured by embeddings is unclear. We show by systematic modeling of non-Euclidean pairwise data that there exists metric violations which can carry valuable problem specific information. Furthermore, Euclidean and non-metric data can be unified on the level of structural information contained in the data. Stable component analysis selects linear subspaces which are particularly insensitive to data fluctuations. Experimental results from different domains support our pattern recognition strategy.},
  language  = {en}
}
@article{KawanabeBlanchardSugiyamaetal.2006,
  author    = {Kawanabe, Motoaki and Blanchard, Gilles and Sugiyama, Masashi and Spokoiny, Vladimir G. and M{\"u}ller, Klaus-Robert},
  title     = {A novel dimension reduction procedure for searching non-Gaussian subspaces},
  issn      = {0302-9743},
  doi       = {10.1007/11679363_19},
  year      = {2006},
  abstract  = {In this article, we consider high-dimensional data which contains a low-dimensional non-Gaussian structure contaminated with Gaussian noise and propose a new linear method to identify the non-Gaussian subspace. Our method NGCA (Non-Gaussian Component Analysis) is based on a very general semi-parametric framework and has a theoretical guarantee that the estimation error of finding the non-Gaussian components tends to zero at a parametric rate. NGCA can be used not only as preprocessing for ICA, but also for extracting and visualizing more general structures like clusters. A numerical study demonstrates the usefulness of our method},
  language  = {en}
}
@article{MeineckeHarmelingMueller2005,
  author    = {Meinecke, Frank C. and Harmeling, Stefan and M{\"u}ller, Klaus-Robert},
  title     = {Inlier-based ICA with an application to superimposed images},
  issn      = {0899-9457},
  year      = {2005},
  abstract  = {This paper proposes a new independent component analysis (ICA) method which is able to unmix overcomplete mixtures of sparce or structured signals like speech, music or images. Furthermore, the method is designed to be robust against outliers, which is a favorable feature for ICA algorithms since most of them are extremely sensitive to outliers. Our approach is based on a simple outlier index. However, instead of robustifying an existing algorithm by some outlier rejection technique we show how this index can be used directly to solve the ICA problem for super-Gaussian sources. The resulting inlier-based ICA (IBICA) is outlier-robust by construction and can be used for standard ICA as well as for overcomplete ICA (i.e. more source signals than observed signals). (c) 2005 Wiley Periodicals, Inc},
  language  = {en}
}
@article{LemmBlankertzCurioetal.2005,
  author    = {Lemm, Steven and Blankertz, Benjamin and Curio, Gabriel and M{\"u}ller, Klaus-Robert},
  title     = {Spatio-spectral filters for improving the classification of single trial EEG},
  issn      = {0018-9294},
  year      = {2005},
  abstract  = {Data recorded in electroencephalogram (EEG)-based brain-computer interface experiments is generally very noisy, non-stationary, and contaminated with artifacts that can deteriorate discrimination/classification methods. In this paper, we extend the common spatial pattern (CSP) algorithm with the aim to alleviate these adverse effects. In particular, we suggest an extension of CSP to the state space, which utilizes the method of time delay embedding. As we will show, this allows for individually tuned frequency filters at each electrode position and, thus, yields an improved and more robust machine learning procedure. The advantages of the proposed method over the original CSP method are verified in terms of an improved information transfer rate (bits per trial) on a set of EEG-recordings from experiments of imagined limb movements},
  language  = {en}
}
@article{DornhegeBlankertzCurioetal.2004,
  author    = {Dornhege, Guido and Blankertz, Benjamin and Curio, Gabriel and M{\"u}ller, Klaus-Robert},
  title     = {Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms},
  year      = {2004},
  abstract  = {Noninvasive electroencephalogram (EEG) recordings provide for easy and safe access to human neocortical processes which can be exploited for a brain-computer interface (BCI). At present, however, the use of BCIs is severely limited by low bit-transfer rates. We systematically analyze and develop two recent concepts, both capable of enhancing the information gain from multichannel scalp EEG recordings: 1) the combination of classifiers, each specifically tailored for different physiological phenomena, e.g., slow cortical potential shifts, such as the premovement Bereitschaftspotential or differences in spatio-spectral distributions of brain activity (i.e., focal event-related desynchronizations) and 2) behavioral paradigms inducing the subjects to generate one out of several brain states (multiclass approach) which all bare a distinctive spatio-temporal signature well discriminable in the standard scalp EEG. We derive information-theoretic predictions and demonstrate their relevance in experimental data. We will show that a suitably arranged interaction between these concepts can significantly boost BCI performances},
  language  = {en}
}